The world's most valuable company crammed a lot into the tablespoon-sized volume of an Apple Watch. There's GPS, a heart-rate sensor, cellular connectivity, and computing resources that not long ago would have filled a desk-dwelling beige box. The wonder gadget doesn't have a sphygmomanometer for measuring blood pressure or polysomnographic equipment found in a sleep lab--but thanks to machine learning, it might be able to help with their work. Research presented at the American Heart Association meeting in Anaheim Monday claims that, when paired with the right machine-learning algorithms, the Apple Watch's heart-rate sensor and step counter can make a fair prediction of whether a person has high blood pressure or sleep apnea, in which breathing stops and starts repeatedly through the night. Both are common--and commonly undiagnosed--conditions associated with life-threatening problems, including stroke and heart attack.

Before modern chemistry brought doctors blood and urine tests for diagnosing diabetes, they had to rely on their taste buds. Sweet-tasting pee has long been the disease's telltale biomarker; mellitus literally means honey. Too much sugar in your bodily fluids means your metabolism has gone haywire--either your cells aren't making insulin or they're not responding to it.

In a new study conducted with the UCSF Department of Medicine, a neural network developed by a startup called Cardiogram was able to detect diabetes with nearly 85 percent accuracy, just by looking at people's heart beats over time. As always, the study didn't require any fancy medical hardware -- just Apple Watches, Fitbits, Android Wear devices, and other wearables with heart rate sensors.